As a hobby I started implementing a simplified Ticket to Ride in Go using graph algorithms with routes as edges, cities as nodes, and the shortest paths algorithm to illustrate gameplay. This year, I wanted to go further and show how AI can make more complex strategies and how image processing can make it visible. We’ll use Go structures to model the board as a graph, simulate gameplay, and compare classic pathfinding (Dijkstra/A*) with a Monte Carlo Tree Search (MCTS) policy and, if time permits, other AI models to choose routes. Then we’ll render the chosen paths on the map, frame‑by‑frame, and assemble animated GIFs that explain decisions as they happen. For teams, this is both an engineering exercise (create a clean architecture, add testing to verify gameplay actions and check for performance issues) and a fun playground for algorithms and visualization. Come for the board games, stay for the AI, and leave with patterns you can reuse in backends, simulations, and other tools.